Quantitative multi-parameter assessment of age- and gender-related variation of back extensor muscles in healthy adults using Dixon MR imaging

Deep-Learning-Based Radiomics to Predict Surgical Risk Factors for Lumbar Disc Herniation in Young Patients: A Multicenter Study

Objective

The aim of this study is to develop and validate a deep-learning radiomics model for predicting surgical risk factors for lumbar disc herniation (LDH) in young patients to assist clinicians in identifying surgical candidates, alleviating symptoms, and improving prognosis.

Methods

A retrospective analysis of patients from two medical centers was conducted. From sagittal and axial MR images, the regions of interest were handcrafted to extract radiomics features. Various machine-learning algorithms were employed and combined with clinical features, resulting in the development of a deep-learning radiomics nomogram (DLRN) to predict surgical risk factors for LDH in young adults. The efficacy of the different models and the clinical benefits of the model were compared.

Results

We derived six sets of features, including clinical features, radiomics features (Rad_SAG and Rad_AXI) and deep learning features (DL_SAG and DL_AXI) from sagittal and axial MR images, as well as fused deep-learning radiomics (DLR) features. The support vector machine(SVM) algorithm exhibited the best performance. The area under the curve (AUC) of DLR in the training and testing cohorts of 0.991 and 0.939, respectively, were significantly better than those of the models developed with radiomics(Rad_SAG=0.914 and 0.863, Rad_AXI=0.927 and 0.85) and deep-learning features(DL_SAG=0.959 and 0.818, DL_AXI=0.960 and 0.811). The AUC of DLRN coupled with clinical features(ODI, Pfirrmann grade, SLRT, MMFI, and MSU classification) were 0.994 and 0.941 in the training and testing cohorts, respectively. Analysis of the calibration and decision curves demonstrated good agreement between the predicted and observed outcomes, and the use of the DLRN to predict the need for surgical treatment of LDH demonstrated significant clinical benefits.

Conclusion

The DLRN established based on clinical and DLR features effectively predicts surgical risk factors for LDH in young adults, offering valuable insights for diagnosis and treatment.

Quality of skeletal muscles during allogeneic stem-cell transplantation: a pilot study

OBJECTIVES

This study investigated the muscle fat fraction (FF) and muscle-related parameters before and after allogeneic hematopoietic stem cell transplantation (HSCT).

METHODS

Fat and water signals were derived from the in-phase and out-of-phase MR signal intensities of the pelvis and thigh using the two-point Dixon technique. They were analysed using Synapse Vincent, and muscle quality was evaluated using the FF. The muscle mass was assessed by measuring the thigh and gluteal muscle areas using a manual trace on the MR image. The association between the muscle FF and clinical data was retrospectively determined.

RESULTS

This study included 11 patients (6 males). Their mean age was 42.7 years, and eight had leukaemia. Eight were assessed at a mean of 65.4 days post-HSCT. The hip and thigh skeletal muscle FFs were not significantly different during HSCT. The grip and lower limb muscle strengths decreased significantly after HSCT. Patients with low FFs before transplantation tended to lose muscle strength, and the increase in FF and decrease of muscle strength were correlated.

CONCLUSIONS

Muscle strength and quantity decrease during the early phase after HSCT, especially in patients with low FF muscles. Therefore, interventions based on muscle quality and quantity are essential.

Assessment of fitting methods and variability of IVIM parameters in muscles of the lumbar spine at rest

Intravoxel incoherent motion (IVIM) MRI provides insight into tissue diffusion and perfusion. Here, estimates of perfusion fraction ( f ), pseudo-diffusion coefficient (D * ), and diffusion coefficient ( D ) obtained via different fitting methods are compared to ascertain (1) the optimal analysis strategy for muscles of the lumbar spine and (2) repeatability of IVIM parameters in skeletal muscle at rest. Diffusion-weighted images were acquired in the lumbar spine at rest in 15 healthy participants. Data were fit to the bi-exponential IVIM model to estimate f , D * and D using three variably segmented approaches based on non-linear least squares fitting, and a Bayesian fitting method. Assuming that perfusion and diffusion are temporally stable in skeletal muscle at rest, and spatially uniform within a spinal segment, the optimal analysis strategy was determined as the approach with the lowest temporal or spatial variation and smallest residual between measured and fit data. Inter-session repeatability of IVIM parameters was evaluated in a subset of 11 people. Finally, simulated IVIM signal at varying signal to noise ratio were evaluated to understand precision and bias. Experimental results showed that IVIM parameter values differed depending on the fitting method. A three-step non-linear least squares fitting approach, where D , f , andD * were estimated sequentially, generally yielded the lowest spatial and temporal variation. Solving all parameters simultaneously yielded the lowest residual between measured and fit data, however there was substantial spatial and temporal variability. Results obtained by Bayesian fitting had high spatial and temporal variability in addition to a large residual between measured and fit data. Simulations showed that all fitting methods can fit the IVIM data at signal to noise ratios >35, and thatD * was the most challenging to accurately obtain. Overall, this study motivates use of a three-step non-linear least squares fitting strategy to quantify IVIM parameters in skeletal muscle.

Association between type 2 diabetes mellitus and body composition based on MRI fat fraction mapping

Purpose

To explore the association between type 2 diabetes mellitus (T2DM) and body composition based on magnetic resonance fat fraction (FF) mapping.

Methods

A total of 341 subjects, who underwent abdominal MRI examination with FF mapping were enrolled in this study, including 68 T2DM patients and 273 non-T2DM patients. The FFs and areas of visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT) and abdominal muscle (AM) were measured at the level of the L1-L2 vertebral. The FF of bone marrow adipose tissue (BMAT) was determined by the averaged FF values measured at the level of T12 and L1 vertebral, respectively. The whole hepatic fat fraction (HFF) and pancreatic fat fraction (PFF) were measured based on 3D semi-automatic segmentation on the FF mapping. All data were analyzed by GraphPad Prism and MedCalc.

Results

VAT area, VAT FF, HFF, PFF of T2DM group were higher than those of non-T2DM group after adjusting for age and sex (P < 0.05). However, there was no differences in SAT area, SAT FF, BMAT FF, AM area and AM FF between the two groups (P > 0.05). VAT area and PFF were independent risk factors of T2DM (all P < 0.05). The area under the curve (AUC) of the receiver operating characteristic (ROC) for VAT area and PFF in differentiating between T2DM and non-T2DM were 0.685 and 0.787, respectively, and the AUC of PFF was higher than VAT area (P < 0.05). Additionally, in seemingly healthy individuals, the SAT area, VAT area, and AM area were found to be significantly associated with being overweight and/or obese (BMI ≥ 25) (all P < 0.05).

Conclusions

In this study, it was found that there were significant associations between T2DM and VAT area, VAT FF, HFF and PFF. In addition, VAT area and PFF were the independent risk factors of T2DM. Especially, PFF showed a high diagnostic performance in discrimination between T2DM and non-T2DM. These findings may highlight the crucial role of PFF in the pathophysiology of T2DM, and it might be served as a potential imaging biomarker of the prevention and treatment of T2DM. Additionally, in individuals without diabetes, focusing on SAT area, VAT area and AM area may help identify potential health risks and provide a basis for targeted weight management and prevention measures.